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- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">from copy import deepcopy
- from typing import List
- import matplotlib.pyplot as plt
- import numpy as np
- import pandas as pd
- from scipy.special import expit
- from sklearn import datasets
- from sklearn import tree
- from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
- from sklearn.model_selection import train_test_split, cross_val_score
- from sklearn.tree import plot_tree, DecisionTreeClassifier
- from sklearn.utils import check_X_y, check_array
- from sklearn.utils.validation import _check_sample_weight, check_is_fitted
- from imodels.tree.viz_utils import extract_sklearn_tree_from_figs
- from imodels.util.arguments import check_fit_arguments
- from imodels.util.data_util import encode_categories
- class Node:
- def __init__(
- self,
- feature: int = None,
- threshold: int = None,
- value=None,
- value_sklearn=None,
- idxs=None,
- is_root: bool = False,
- left=None,
- impurity: float = None,
- impurity_reduction: float = None,
- tree_num: int = None,
- node_id: int = None,
- right=None,
- ):
- """Node class for splitting"""
- # split or linear
- self.is_root = is_root
- self.idxs = idxs
- self.tree_num = tree_num
- self.node_id = None
- self.feature = feature
- self.impurity = impurity
- self.impurity_reduction = impurity_reduction
- self.value_sklearn = value_sklearn
- # different meanings
- self.value = value # for split this is mean, for linear this is weight
- # split-specific
- self.threshold = threshold
- self.left = left
- self.right = right
- self.left_temp = None
- self.right_temp = None
- def setattrs(self, **kwargs):
- for k, v in kwargs.items():
- setattr(self, k, v)
- def __str__(self):
- if self.is_root:
- return f"X_{self.feature} <= {self.threshold:0.3f} (Tree #{self.tree_num} root)"
- elif self.left is None and self.right is None:
- return f"Val: {self.value[0][0]:0.3f} (leaf)"
- else:
- return f"X_{self.feature} <= {self.threshold:0.3f} (split)"
- def print_root(self, y):
- try:
- one_count = pd.Series(y).value_counts()[1.0]
- except KeyError:
- one_count = 0
- one_proportion = (
- f" {one_count}/{y.shape[0]} ({round(100 * one_count / y.shape[0], 2)}%)"
- )
- if self.is_root:
- return f"X_{self.feature} <= {self.threshold:0.3f}" + one_proportion
- elif self.left is None and self.right is None:
- return f"ΔRisk = {self.value[0][0]:0.2f}" + one_proportion
- else:
- return f"X_{self.feature} <= {self.threshold:0.3f}" + one_proportion
- def __repr__(self):
- return self.__str__()
- class FIGS(BaseEstimator):
- """FIGS (sum of trees) classifier.
- Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
- Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
- The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
- Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
- https://arxiv.org/abs/2201.11931
- """
- def __init__(
- self,
- max_rules: int = 12,
- max_trees: int = None,
- min_impurity_decrease: float = 0.0,
- random_state=None,
- max_features: str = None,
- ):
- """
- Params
- ------
- max_rules: int
- Max total number of rules across all trees
- max_trees: int
- Max total number of trees
- min_impurity_decrease: float
- A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
- max_features
- The number of features to consider when looking for the best split (see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
- """
- super().__init__()
- self.max_rules = max_rules
- self.max_trees = max_trees
- self.min_impurity_decrease = min_impurity_decrease
- self.random_state = random_state
- self.max_features = max_features
- self._init_decision_function()
- def _init_decision_function(self):
- """Sets decision function based on _estimator_type"""
- # used by sklearn GridSearchCV, BaggingClassifier
- if isinstance(self, ClassifierMixin):
- def decision_function(x):
- return self.predict_proba(x)[:, 1]
- elif isinstance(self, RegressorMixin):
- decision_function = self.predict
- def _construct_node_with_stump(
- self,
- X,
- y,
- idxs,
- tree_num,
- sample_weight=None,
- compare_nodes_with_sample_weight=True,
- max_features=None,
- ):
- """
- Params
- ------
- compare_nodes_with_sample_weight: Deprecated
- If this is set to true and sample_weight is passed, use sample_weight to compare nodes
- Otherwise, use sample_weight only for picking a split given a particular node
- """
- # array indices
- SPLIT = 0
- LEFT = 1
- RIGHT = 2
- # fit stump
- stump = tree.DecisionTreeRegressor(max_depth=1, max_features=max_features)
- sweight = None
- if sample_weight is not None:
- sweight = sample_weight[idxs]
- stump.fit(X[idxs], y[idxs], sample_weight=sweight)
- # these are all arrays, arr[0] is split node
- # note: -2 is dummy
- feature = stump.tree_.feature
- threshold = stump.tree_.threshold
- impurity = stump.tree_.impurity
- n_node_samples = stump.tree_.n_node_samples
- value = stump.tree_.value
- # no split
- if len(feature) == 1:
- # print('no split found!', idxs.sum(), impurity, feature)
- return Node(
- idxs=idxs,
- value=value[SPLIT],
- tree_num=tree_num,
- feature=feature[SPLIT],
- threshold=threshold[SPLIT],
- impurity=impurity[SPLIT],
- impurity_reduction=None,
- )
- # manage sample weights
- idxs_split = X[:, feature[SPLIT]] <= threshold[SPLIT]
- idxs_left = idxs_split & idxs
- idxs_right = ~idxs_split & idxs
- if sample_weight is None:
- n_node_samples_left = n_node_samples[LEFT]
- n_node_samples_right = n_node_samples[RIGHT]
- else:
- n_node_samples_left = sample_weight[idxs_left].sum()
- n_node_samples_right = sample_weight[idxs_right].sum()
- n_node_samples_split = n_node_samples_left + n_node_samples_right
- # calculate impurity
- impurity_reduction = (
- impurity[SPLIT]
- - impurity[LEFT] * n_node_samples_left / n_node_samples_split
- - impurity[RIGHT] * n_node_samples_right / n_node_samples_split
- ) * n_node_samples_split
- node_split = Node(
- idxs=idxs,
- value=value[SPLIT],
- tree_num=tree_num,
- feature=feature[SPLIT],
- threshold=threshold[SPLIT],
- impurity=impurity[SPLIT],
- impurity_reduction=impurity_reduction,
- )
- # print('\t>>>', node_split, 'impurity', impurity, 'num_pts', idxs.sum(), 'imp_reduc', impurity_reduction)
- # manage children
- node_left = Node(
- idxs=idxs_left,
- value=value[LEFT],
- impurity=impurity[LEFT],
- tree_num=tree_num,
- )
- node_right = Node(
- idxs=idxs_right,
- value=value[RIGHT],
- impurity=impurity[RIGHT],
- tree_num=tree_num,
- )
- node_split.setattrs(
- left_temp=node_left,
- right_temp=node_right,
- )
- return node_split
- def _encode_categories(self, X, categorical_features):
- encoder = None
- if hasattr(self, "_encoder"):
- encoder = self._encoder
- return encode_categories(X, categorical_features, encoder)
- def fit(
- self,
- X,
- y=None,
- feature_names=None,
- verbose=False,
- sample_weight=None,
- categorical_features=None,
- ):
- """
- Params
- ------
- _sample_weight: array-like of shape (n_samples,), default=None
- Sample weights. If None, then samples are equally weighted.
- Splits that would create child nodes with net zero or negative weight
- are ignored while searching for a split in each node.
- """
- if categorical_features is not None:
- X, self._encoder = self._encode_categories(X, categorical_features)
- X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
- if sample_weight is not None:
- sample_weight = _check_sample_weight(sample_weight, X)
- self.trees_ = [] # list of the root nodes of added trees
- self.complexity_ = 0 # tracks the number of rules in the model
- y_predictions_per_tree = {} # predictions for each tree
- y_residuals_per_tree = {} # based on predictions above
- # set up initial potential_splits
- # everything in potential_splits either is_root (so it can be added directly to self.trees_)
- # or it is a child of a root node that has already been added
- idxs = np.ones(X.shape[0], dtype=bool)
- node_init = self._construct_node_with_stump(
- X=X,
- y=y,
- idxs=idxs,
- tree_num=-1,
- sample_weight=sample_weight,
- max_features=self.max_features,
- )
- potential_splits = [node_init]
- for node in potential_splits:
- node.setattrs(is_root=True)
- potential_splits = sorted(potential_splits, key=lambda x: x.impurity_reduction)
- # start the greedy fitting algorithm
- finished = False
- while len(potential_splits) > 0 and not finished:
- # print('potential_splits', [str(s) for s in potential_splits])
- # get node with max impurity_reduction (since it's sorted)
- split_node = potential_splits.pop()
- # don't split on node
- if split_node.impurity_reduction < self.min_impurity_decrease:
- finished = True
- break
- elif (
- split_node.is_root
- and self.max_trees is not None
- and len(self.trees_) >= self.max_trees
- ):
- # If the node is the root of a new tree and we have reached self.max_trees,
- # don't split on it, but allow later splits to continue growing existing trees
- continue
- # split on node
- if verbose:
- print("\nadding " + str(split_node))
- self.complexity_ += 1
- # if added a tree root
- if split_node.is_root:
- # start a new tree
- self.trees_.append(split_node)
- # update tree_num
- for node_ in [split_node, split_node.left_temp, split_node.right_temp]:
- if node_ is not None:
- node_.tree_num = len(self.trees_) - 1
- # add new root potential node
- node_new_root = Node(
- is_root=True, idxs=np.ones(X.shape[0], dtype=bool), tree_num=-1
- )
- potential_splits.append(node_new_root)
- # add children to potential splits
- # assign left_temp, right_temp to be proper children
- # (basically adds them to tree in predict method)
- split_node.setattrs(left=split_node.left_temp, right=split_node.right_temp)
- # add children to potential_splits
- potential_splits.append(split_node.left)
- potential_splits.append(split_node.right)
- # update predictions for altered tree
- for tree_num_ in range(len(self.trees_)):
- y_predictions_per_tree[tree_num_] = self._predict_tree(
- self.trees_[tree_num_], X
- )
- # dummy 0 preds for possible new trees
- y_predictions_per_tree[-1] = np.zeros(X.shape[0])
- # update residuals for each tree
- # -1 is key for potential new tree
- for tree_num_ in list(range(len(self.trees_))) + [-1]:
- y_residuals_per_tree[tree_num_] = deepcopy(y)
- # subtract predictions of all other trees
- # Since the current tree makes a constant prediction over the node being split,
- # one may ignore its contributions to the residuals without affecting the impurity decrease.
- for tree_num_other_ in range(len(self.trees_)):
- if not tree_num_other_ == tree_num_:
- y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[
- tree_num_other_
- ]
- # recompute all impurities + update potential_split children
- potential_splits_new = []
- for potential_split in potential_splits:
- y_target = y_residuals_per_tree[potential_split.tree_num]
- # re-calculate the best split
- potential_split_updated = self._construct_node_with_stump(
- X=X,
- y=y_target,
- idxs=potential_split.idxs,
- tree_num=potential_split.tree_num,
- sample_weight=sample_weight,
- max_features=self.max_features,
- )
- # need to preserve certain attributes from before (value at this split + is_root)
- # value may change because residuals may have changed, but we want it to store the value from before
- potential_split.setattrs(
- feature=potential_split_updated.feature,
- threshold=potential_split_updated.threshold,
- impurity_reduction=potential_split_updated.impurity_reduction,
- impurity=potential_split_updated.impurity,
- left_temp=potential_split_updated.left_temp,
- right_temp=potential_split_updated.right_temp,
- )
- # this is a valid split
- if potential_split.impurity_reduction is not None:
- potential_splits_new.append(potential_split)
- # sort so largest impurity reduction comes last (should probs make this a heap later)
- potential_splits = sorted(
- potential_splits_new, key=lambda x: x.impurity_reduction
- )
- if verbose:
- print(self)
- if self.max_rules is not None and self.complexity_ >= self.max_rules:
- finished = True
- break
- # annotate final tree with node_id and value_sklearn, and prepare importance_data_
- importance_data = []
- for tree_ in self.trees_:
- node_counter = iter(range(0, int(1e06)))
- def _annotate_node(node: Node, X, y):
- if node is None:
- return
- # TODO does not incorporate sample weights
- value_counts = pd.Series(y).value_counts()
- try:
- neg_count = value_counts[0.0]
- except KeyError:
- neg_count = 0
- try:
- pos_count = value_counts[1.0]
- except KeyError:
- pos_count = 0
- value_sklearn = np.array([neg_count, pos_count], dtype=float)
- node.setattrs(node_id=next(node_counter), value_sklearn=value_sklearn)
- idxs_left = X[:, node.feature] <= node.threshold
- _annotate_node(node.left, X[idxs_left], y[idxs_left])
- _annotate_node(node.right, X[~idxs_left], y[~idxs_left])
- _annotate_node(tree_, X, y)
- # now that the samples per node are known, we can start to compute the importances
- importance_data_tree = np.zeros(len(self.feature_names_))
- def _importances(node: Node):
- if node is None or node.left is None:
- return 0.0
- # TODO does not incorporate sample weights, but will if added to value_sklearn
- importance_data_tree[node.feature] += (
- np.sum(node.value_sklearn) * node.impurity
- - np.sum(node.left.value_sklearn) * node.left.impurity
- - np.sum(node.right.value_sklearn) * node.right.impurity
- )
- return (
- np.sum(node.value_sklearn)
- + _importances(node.left)
- + _importances(node.right)
- )
- # require the tree to have more than 1 node, otherwise just leave importance_data_tree as zeros
- if 1 < next(node_counter):
- tree_samples = _importances(tree_)
- if tree_samples != 0:
- importance_data_tree /= tree_samples
- else:
- importance_data_tree = 0
- importance_data.append(importance_data_tree)
- self.importance_data_ = importance_data
- return self
- def _tree_to_str(self, root: Node, prefix=""):
- if root is None:
- return ""
- elif root.threshold is None:
- return ""
- pprefix = prefix + "\t"
- return (
- prefix
- + str(root)
- + "\n"
- + self._tree_to_str(root.left, pprefix)
- + self._tree_to_str(root.right, pprefix)
- )
- def _tree_to_str_with_data(self, X, y, root: Node, prefix=""):
- if root is None:
- return ""
- elif root.threshold is None:
- return ""
- pprefix = prefix + "\t"
- left = X[:, root.feature] <= root.threshold
- return (
- prefix
- + root.print_root(y)
- + "\n"
- + self._tree_to_str_with_data(X[left], y[left], root.left, pprefix)
- + self._tree_to_str_with_data(X[~left], y[~left], root.right, pprefix)
- )
- def __str__(self):
- if not hasattr(self, "trees_"):
- s = self.__class__.__name__
- s += "("
- s += "max_rules="
- s += repr(self.max_rules)
- s += ")"
- return s
- else:
- s = "> ------------------------------\n"
- s += "> FIGS-Fast Interpretable Greedy-Tree Sums:\n"
- s += '> \tPredictions are made by summing the "Val" reached by traversing each tree.\n'
- s += "> \tFor classifiers, a sigmoid function is then applied to the sum.\n"
- s += "> ------------------------------\n"
- s += "\n\t+\n".join([self._tree_to_str(t) for t in self.trees_])
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- for i in range(len(self.feature_names_))[::-1]:
- s = s.replace(f"X_{i}", self.feature_names_[i])
- return s
- def print_tree(self, X, y, feature_names=None):
- s = "------------\n" + "\n\t+\n".join(
- [self._tree_to_str_with_data(X, y, t) for t in self.trees_]
- )
- if feature_names is None:
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- feature_names = self.feature_names_
- if feature_names is not None:
- for i in range(len(feature_names))[::-1]:
- s = s.replace(f"X_{i}", feature_names[i])
- return s
- def predict(self, X, categorical_features=None):
- if hasattr(self, "_encoder"):
- X = self._encode_categories(X, categorical_features=categorical_features)
- X = check_array(X)
- preds = np.zeros(X.shape[0])
- for tree in self.trees_:
- preds += self._predict_tree(tree, X)
- if isinstance(self, RegressorMixin):
- return preds
- elif isinstance(self, ClassifierMixin):
- return (preds > 0.5).astype(int)
- def predict_proba(self, X, categorical_features=None, use_clipped_prediction=False):
- """Predict probability for classifiers:
- Default behavior is to constrain the outputs to the range of probabilities, i.e. 0 to 1, with a sigmoid function.
- Set use_clipped_prediction=True to use prior behavior of clipping between 0 and 1 instead.
- """
- if hasattr(self, "_encoder"):
- X = self._encode_categories(X, categorical_features=categorical_features)
- X = check_array(X)
- if isinstance(self, RegressorMixin):
- return NotImplemented
- preds = np.zeros(X.shape[0])
- for tree in self.trees_:
- preds += self._predict_tree(tree, X)
- if use_clipped_prediction:
- # old behavior, pre v1.3.9
- # constrain to range of probabilities by clipping
- preds = np.clip(preds, a_min=0.0, a_max=1.0)
- else:
- # constrain to range of probabilities with a sigmoid function
- preds = expit(preds)
- return np.vstack((1 - preds, preds)).transpose()
- def _predict_tree(self, root: Node, X):
- """Predict for a single tree"""
- def _predict_tree_single_point(root: Node, x):
- if root.left is None and root.right is None:
- return root.value[0, 0]
- left = x[root.feature] <= root.threshold
- if left:
- if root.left is None: # we don't actually have to worry about this case
- return root.value
- else:
- return _predict_tree_single_point(root.left, x)
- else:
- if (
- root.right is None
- ): # we don't actually have to worry about this case
- return root.value
- else:
- return _predict_tree_single_point(root.right, x)
- preds = np.zeros(X.shape[0])
- for i in range(X.shape[0]):
- preds[i] = _predict_tree_single_point(root, X[i])
- return preds
- @property
- def feature_importances_(self):
- """Gini impurity-based feature importances"""
- check_is_fitted(self)
- avg_feature_importances = np.mean(
- self.importance_data_, axis=0, dtype=np.float64
- )
- return avg_feature_importances / np.sum(avg_feature_importances)
- def plot(
- self,
- cols=2,
- feature_names=None,
- filename=None,
- label="all",
- impurity=False,
- tree_number=None,
- dpi=150,
- fig_size=None,
- ):
- is_single_tree = len(self.trees_) < 2 or tree_number is not None
- n_cols = int(cols)
- n_rows = int(np.ceil(len(self.trees_) / n_cols))
- if feature_names is None:
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- feature_names = self.feature_names_
- n_plots = int(len(self.trees_)) if tree_number is None else 1
- fig, axs = plt.subplots(n_plots, dpi=dpi)
- if fig_size is not None:
- fig.set_size_inches(fig_size, fig_size)
- n_classes = 1 if isinstance(self, RegressorMixin) else 2
- ax_size = int(len(self.trees_))
- for i in range(n_plots):
- r = i // n_cols
- c = i % n_cols
- if not is_single_tree:
- ax = axs[i]
- else:
- ax = axs
- try:
- dt = extract_sklearn_tree_from_figs(
- self, i if tree_number is None else tree_number, n_classes
- )
- plot_tree(
- dt,
- ax=ax,
- feature_names=feature_names,
- label=label,
- impurity=impurity,
- )
- except IndexError:
- ax.axis("off")
- continue
- ttl = f"Tree {i}" if n_plots > 1 else f"Tree {tree_number}"
- ax.set_title(ttl)
- if filename is not None:
- plt.savefig(filename)
- return
- plt.show()
- class FIGSRegressor(FIGS, RegressorMixin):
- ...
- class FIGSClassifier(FIGS, ClassifierMixin):
- ...
- class FIGSCV:
- def __init__(
- self,
- figs,
- n_rules_list: List[int] = [6, 12, 24, 30, 50],
- n_trees_list: List[int] = [5, 5, 5, 5, 5],
- cv: int = 3,
- scoring=None,
- *args,
- **kwargs,
- ):
- if len(n_rules_list) != len(n_trees_list):
- raise ValueError(
- f"len(n_rules_list) = {len(n_rules_list)} != len(n_trees_list) = {len(n_trees_list)}"
- )
- self._figs_class = figs
- self.n_rules_list = np.array(n_rules_list)
- self.n_trees_list = np.array(n_trees_list)
- self.cv = cv
- self.scoring = scoring
- def fit(self, X, y):
- self.scores_ = []
- for _i, n_rules in enumerate(self.n_rules_list):
- est = self._figs_class(max_rules=n_rules, max_trees=self.n_trees_list[_i])
- cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
- mean_score = np.mean(cv_scores)
- if len(self.scores_) == 0:
- self.figs = est
- elif mean_score > np.max(self.scores_):
- self.figs = est
- self.scores_.append(mean_score)
- self.figs.fit(X=X, y=y)
- def predict_proba(self, X):
- return self.figs.predict_proba(X)
- def predict(self, X):
- return self.figs.predict(X)
- @property
- def max_rules(self):
- return self.figs.max_rules
- @property
- def max_trees(self):
- return self.figs.max_trees
- class FIGSRegressorCV(FIGSCV):
- def __init__(
- self,
- n_rules_list: List[int] = [6, 12, 24, 30, 50],
- n_trees_list: List[int] = [5, 5, 5, 5, 5],
- cv: int = 3,
- scoring="r2",
- *args,
- **kwargs,
- ):
- super(FIGSRegressorCV, self).__init__(
- figs=FIGSRegressor,
- n_rules_list=n_rules_list,
- n_trees_list=n_trees_list,
- cv=cv,
- scoring=scoring,
- *args,
- **kwargs,
- )
- class FIGSClassifierCV(FIGSCV):
- def __init__(
- self,
- n_rules_list: List[int] = [6, 12, 24, 30, 50],
- n_trees_list: List[int] = [5, 5, 5, 5, 5],
- cv: int = 3,
- scoring="accuracy",
- *args,
- **kwargs,
- ):
- super(FIGSClassifierCV, self).__init__(
- figs=FIGSClassifier,
- n_rules_list=n_rules_list,
- n_trees_list=n_trees_list,
- cv=cv,
- scoring=scoring,
- *args,
- **kwargs,
- )
- if __name__ == "__main__":
- from sklearn import datasets
- X_cls, Y_cls = datasets.load_breast_cancer(return_X_y=True)
- X_reg, Y_reg = datasets.make_friedman1(100)
- categories = ["cat", "dog", "bird", "fish"]
- categories_2 = ["bear", "chicken", "cow"]
- X_cat = pd.DataFrame(X_reg)
- X_cat["pet1"] = np.random.choice(categories, size=(100, 1))
- X_cat["pet2"] = np.random.choice(categories_2, size=(100, 1))
- # X_cat.columns[-1] = "pet"
- Y_cat = Y_reg
- est = FIGSRegressor(max_rules=10)
- est.fit(X_cat, Y_cat, categorical_features=["pet1", "pet2"])
- est.predict(X_cat, categorical_features=["pet1", "pet2"])
- est.plot(tree_number=1)
- est = FIGSClassifier(max_rules=10)
- # est.fit(X_cls, Y_cls, sample_weight=np.arange(0, X_cls.shape[0]))
- est.fit(X_cls, Y_cls, sample_weight=[1] * X_cls.shape[0])
- est.predict(X_cls)
- est = FIGSRegressorCV()
- est.fit(X_reg, Y_reg)
- est.predict(X_reg)
- print(est.max_rules)
- est.figs.plot(tree_number=0)
- est = FIGSClassifierCV()
- est.fit(X_cls, Y_cls)
- est.predict(X_cls)
- print(est.max_rules)
- est.figs.plot(tree_number=0)
- # %%</code></pre>
- </details>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- </section>
- <section>
- <h2 class="section-title" id="header-classes">Classes</h2>
- <dl>
- <dt id="imodels.tree.figs.FIGS"><code class="flex name class">
- <span>class <span class="ident">FIGS</span></span>
- <span>(</span><span>max_rules: int = 12, max_trees: int = None, min_impurity_decrease: float = 0.0, random_state=None, max_features: str = None)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>FIGS (sum of trees) classifier.
- Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
- Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
- The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
- Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
- <a href="https://arxiv.org/abs/2201.11931">https://arxiv.org/abs/2201.11931</a></p>
- <h2 id="params">Params</h2>
- <p>max_rules: int
- Max total number of rules across all trees
- max_trees: int
- Max total number of trees
- min_impurity_decrease: float
- A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
- max_features
- The number of features to consider when looking for the best split (see <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html">https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html</a>)</p></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class FIGS(BaseEstimator):
- """FIGS (sum of trees) classifier.
- Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
- Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
- The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
- Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
- https://arxiv.org/abs/2201.11931
- """
- def __init__(
- self,
- max_rules: int = 12,
- max_trees: int = None,
- min_impurity_decrease: float = 0.0,
- random_state=None,
- max_features: str = None,
- ):
- """
- Params
- ------
- max_rules: int
- Max total number of rules across all trees
- max_trees: int
- Max total number of trees
- min_impurity_decrease: float
- A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
- max_features
- The number of features to consider when looking for the best split (see https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
- """
- super().__init__()
- self.max_rules = max_rules
- self.max_trees = max_trees
- self.min_impurity_decrease = min_impurity_decrease
- self.random_state = random_state
- self.max_features = max_features
- self._init_decision_function()
- def _init_decision_function(self):
- """Sets decision function based on _estimator_type"""
- # used by sklearn GridSearchCV, BaggingClassifier
- if isinstance(self, ClassifierMixin):
- def decision_function(x):
- return self.predict_proba(x)[:, 1]
- elif isinstance(self, RegressorMixin):
- decision_function = self.predict
- def _construct_node_with_stump(
- self,
- X,
- y,
- idxs,
- tree_num,
- sample_weight=None,
- compare_nodes_with_sample_weight=True,
- max_features=None,
- ):
- """
- Params
- ------
- compare_nodes_with_sample_weight: Deprecated
- If this is set to true and sample_weight is passed, use sample_weight to compare nodes
- Otherwise, use sample_weight only for picking a split given a particular node
- """
- # array indices
- SPLIT = 0
- LEFT = 1
- RIGHT = 2
- # fit stump
- stump = tree.DecisionTreeRegressor(max_depth=1, max_features=max_features)
- sweight = None
- if sample_weight is not None:
- sweight = sample_weight[idxs]
- stump.fit(X[idxs], y[idxs], sample_weight=sweight)
- # these are all arrays, arr[0] is split node
- # note: -2 is dummy
- feature = stump.tree_.feature
- threshold = stump.tree_.threshold
- impurity = stump.tree_.impurity
- n_node_samples = stump.tree_.n_node_samples
- value = stump.tree_.value
- # no split
- if len(feature) == 1:
- # print('no split found!', idxs.sum(), impurity, feature)
- return Node(
- idxs=idxs,
- value=value[SPLIT],
- tree_num=tree_num,
- feature=feature[SPLIT],
- threshold=threshold[SPLIT],
- impurity=impurity[SPLIT],
- impurity_reduction=None,
- )
- # manage sample weights
- idxs_split = X[:, feature[SPLIT]] <= threshold[SPLIT]
- idxs_left = idxs_split & idxs
- idxs_right = ~idxs_split & idxs
- if sample_weight is None:
- n_node_samples_left = n_node_samples[LEFT]
- n_node_samples_right = n_node_samples[RIGHT]
- else:
- n_node_samples_left = sample_weight[idxs_left].sum()
- n_node_samples_right = sample_weight[idxs_right].sum()
- n_node_samples_split = n_node_samples_left + n_node_samples_right
- # calculate impurity
- impurity_reduction = (
- impurity[SPLIT]
- - impurity[LEFT] * n_node_samples_left / n_node_samples_split
- - impurity[RIGHT] * n_node_samples_right / n_node_samples_split
- ) * n_node_samples_split
- node_split = Node(
- idxs=idxs,
- value=value[SPLIT],
- tree_num=tree_num,
- feature=feature[SPLIT],
- threshold=threshold[SPLIT],
- impurity=impurity[SPLIT],
- impurity_reduction=impurity_reduction,
- )
- # print('\t>>>', node_split, 'impurity', impurity, 'num_pts', idxs.sum(), 'imp_reduc', impurity_reduction)
- # manage children
- node_left = Node(
- idxs=idxs_left,
- value=value[LEFT],
- impurity=impurity[LEFT],
- tree_num=tree_num,
- )
- node_right = Node(
- idxs=idxs_right,
- value=value[RIGHT],
- impurity=impurity[RIGHT],
- tree_num=tree_num,
- )
- node_split.setattrs(
- left_temp=node_left,
- right_temp=node_right,
- )
- return node_split
- def _encode_categories(self, X, categorical_features):
- encoder = None
- if hasattr(self, "_encoder"):
- encoder = self._encoder
- return encode_categories(X, categorical_features, encoder)
- def fit(
- self,
- X,
- y=None,
- feature_names=None,
- verbose=False,
- sample_weight=None,
- categorical_features=None,
- ):
- """
- Params
- ------
- _sample_weight: array-like of shape (n_samples,), default=None
- Sample weights. If None, then samples are equally weighted.
- Splits that would create child nodes with net zero or negative weight
- are ignored while searching for a split in each node.
- """
- if categorical_features is not None:
- X, self._encoder = self._encode_categories(X, categorical_features)
- X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
- if sample_weight is not None:
- sample_weight = _check_sample_weight(sample_weight, X)
- self.trees_ = [] # list of the root nodes of added trees
- self.complexity_ = 0 # tracks the number of rules in the model
- y_predictions_per_tree = {} # predictions for each tree
- y_residuals_per_tree = {} # based on predictions above
- # set up initial potential_splits
- # everything in potential_splits either is_root (so it can be added directly to self.trees_)
- # or it is a child of a root node that has already been added
- idxs = np.ones(X.shape[0], dtype=bool)
- node_init = self._construct_node_with_stump(
- X=X,
- y=y,
- idxs=idxs,
- tree_num=-1,
- sample_weight=sample_weight,
- max_features=self.max_features,
- )
- potential_splits = [node_init]
- for node in potential_splits:
- node.setattrs(is_root=True)
- potential_splits = sorted(potential_splits, key=lambda x: x.impurity_reduction)
- # start the greedy fitting algorithm
- finished = False
- while len(potential_splits) > 0 and not finished:
- # print('potential_splits', [str(s) for s in potential_splits])
- # get node with max impurity_reduction (since it's sorted)
- split_node = potential_splits.pop()
- # don't split on node
- if split_node.impurity_reduction < self.min_impurity_decrease:
- finished = True
- break
- elif (
- split_node.is_root
- and self.max_trees is not None
- and len(self.trees_) >= self.max_trees
- ):
- # If the node is the root of a new tree and we have reached self.max_trees,
- # don't split on it, but allow later splits to continue growing existing trees
- continue
- # split on node
- if verbose:
- print("\nadding " + str(split_node))
- self.complexity_ += 1
- # if added a tree root
- if split_node.is_root:
- # start a new tree
- self.trees_.append(split_node)
- # update tree_num
- for node_ in [split_node, split_node.left_temp, split_node.right_temp]:
- if node_ is not None:
- node_.tree_num = len(self.trees_) - 1
- # add new root potential node
- node_new_root = Node(
- is_root=True, idxs=np.ones(X.shape[0], dtype=bool), tree_num=-1
- )
- potential_splits.append(node_new_root)
- # add children to potential splits
- # assign left_temp, right_temp to be proper children
- # (basically adds them to tree in predict method)
- split_node.setattrs(left=split_node.left_temp, right=split_node.right_temp)
- # add children to potential_splits
- potential_splits.append(split_node.left)
- potential_splits.append(split_node.right)
- # update predictions for altered tree
- for tree_num_ in range(len(self.trees_)):
- y_predictions_per_tree[tree_num_] = self._predict_tree(
- self.trees_[tree_num_], X
- )
- # dummy 0 preds for possible new trees
- y_predictions_per_tree[-1] = np.zeros(X.shape[0])
- # update residuals for each tree
- # -1 is key for potential new tree
- for tree_num_ in list(range(len(self.trees_))) + [-1]:
- y_residuals_per_tree[tree_num_] = deepcopy(y)
- # subtract predictions of all other trees
- # Since the current tree makes a constant prediction over the node being split,
- # one may ignore its contributions to the residuals without affecting the impurity decrease.
- for tree_num_other_ in range(len(self.trees_)):
- if not tree_num_other_ == tree_num_:
- y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[
- tree_num_other_
- ]
- # recompute all impurities + update potential_split children
- potential_splits_new = []
- for potential_split in potential_splits:
- y_target = y_residuals_per_tree[potential_split.tree_num]
- # re-calculate the best split
- potential_split_updated = self._construct_node_with_stump(
- X=X,
- y=y_target,
- idxs=potential_split.idxs,
- tree_num=potential_split.tree_num,
- sample_weight=sample_weight,
- max_features=self.max_features,
- )
- # need to preserve certain attributes from before (value at this split + is_root)
- # value may change because residuals may have changed, but we want it to store the value from before
- potential_split.setattrs(
- feature=potential_split_updated.feature,
- threshold=potential_split_updated.threshold,
- impurity_reduction=potential_split_updated.impurity_reduction,
- impurity=potential_split_updated.impurity,
- left_temp=potential_split_updated.left_temp,
- right_temp=potential_split_updated.right_temp,
- )
- # this is a valid split
- if potential_split.impurity_reduction is not None:
- potential_splits_new.append(potential_split)
- # sort so largest impurity reduction comes last (should probs make this a heap later)
- potential_splits = sorted(
- potential_splits_new, key=lambda x: x.impurity_reduction
- )
- if verbose:
- print(self)
- if self.max_rules is not None and self.complexity_ >= self.max_rules:
- finished = True
- break
- # annotate final tree with node_id and value_sklearn, and prepare importance_data_
- importance_data = []
- for tree_ in self.trees_:
- node_counter = iter(range(0, int(1e06)))
- def _annotate_node(node: Node, X, y):
- if node is None:
- return
- # TODO does not incorporate sample weights
- value_counts = pd.Series(y).value_counts()
- try:
- neg_count = value_counts[0.0]
- except KeyError:
- neg_count = 0
- try:
- pos_count = value_counts[1.0]
- except KeyError:
- pos_count = 0
- value_sklearn = np.array([neg_count, pos_count], dtype=float)
- node.setattrs(node_id=next(node_counter), value_sklearn=value_sklearn)
- idxs_left = X[:, node.feature] <= node.threshold
- _annotate_node(node.left, X[idxs_left], y[idxs_left])
- _annotate_node(node.right, X[~idxs_left], y[~idxs_left])
- _annotate_node(tree_, X, y)
- # now that the samples per node are known, we can start to compute the importances
- importance_data_tree = np.zeros(len(self.feature_names_))
- def _importances(node: Node):
- if node is None or node.left is None:
- return 0.0
- # TODO does not incorporate sample weights, but will if added to value_sklearn
- importance_data_tree[node.feature] += (
- np.sum(node.value_sklearn) * node.impurity
- - np.sum(node.left.value_sklearn) * node.left.impurity
- - np.sum(node.right.value_sklearn) * node.right.impurity
- )
- return (
- np.sum(node.value_sklearn)
- + _importances(node.left)
- + _importances(node.right)
- )
- # require the tree to have more than 1 node, otherwise just leave importance_data_tree as zeros
- if 1 < next(node_counter):
- tree_samples = _importances(tree_)
- if tree_samples != 0:
- importance_data_tree /= tree_samples
- else:
- importance_data_tree = 0
- importance_data.append(importance_data_tree)
- self.importance_data_ = importance_data
- return self
- def _tree_to_str(self, root: Node, prefix=""):
- if root is None:
- return ""
- elif root.threshold is None:
- return ""
- pprefix = prefix + "\t"
- return (
- prefix
- + str(root)
- + "\n"
- + self._tree_to_str(root.left, pprefix)
- + self._tree_to_str(root.right, pprefix)
- )
- def _tree_to_str_with_data(self, X, y, root: Node, prefix=""):
- if root is None:
- return ""
- elif root.threshold is None:
- return ""
- pprefix = prefix + "\t"
- left = X[:, root.feature] <= root.threshold
- return (
- prefix
- + root.print_root(y)
- + "\n"
- + self._tree_to_str_with_data(X[left], y[left], root.left, pprefix)
- + self._tree_to_str_with_data(X[~left], y[~left], root.right, pprefix)
- )
- def __str__(self):
- if not hasattr(self, "trees_"):
- s = self.__class__.__name__
- s += "("
- s += "max_rules="
- s += repr(self.max_rules)
- s += ")"
- return s
- else:
- s = "> ------------------------------\n"
- s += "> FIGS-Fast Interpretable Greedy-Tree Sums:\n"
- s += '> \tPredictions are made by summing the "Val" reached by traversing each tree.\n'
- s += "> \tFor classifiers, a sigmoid function is then applied to the sum.\n"
- s += "> ------------------------------\n"
- s += "\n\t+\n".join([self._tree_to_str(t) for t in self.trees_])
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- for i in range(len(self.feature_names_))[::-1]:
- s = s.replace(f"X_{i}", self.feature_names_[i])
- return s
- def print_tree(self, X, y, feature_names=None):
- s = "------------\n" + "\n\t+\n".join(
- [self._tree_to_str_with_data(X, y, t) for t in self.trees_]
- )
- if feature_names is None:
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- feature_names = self.feature_names_
- if feature_names is not None:
- for i in range(len(feature_names))[::-1]:
- s = s.replace(f"X_{i}", feature_names[i])
- return s
- def predict(self, X, categorical_features=None):
- if hasattr(self, "_encoder"):
- X = self._encode_categories(X, categorical_features=categorical_features)
- X = check_array(X)
- preds = np.zeros(X.shape[0])
- for tree in self.trees_:
- preds += self._predict_tree(tree, X)
- if isinstance(self, RegressorMixin):
- return preds
- elif isinstance(self, ClassifierMixin):
- return (preds > 0.5).astype(int)
- def predict_proba(self, X, categorical_features=None, use_clipped_prediction=False):
- """Predict probability for classifiers:
- Default behavior is to constrain the outputs to the range of probabilities, i.e. 0 to 1, with a sigmoid function.
- Set use_clipped_prediction=True to use prior behavior of clipping between 0 and 1 instead.
- """
- if hasattr(self, "_encoder"):
- X = self._encode_categories(X, categorical_features=categorical_features)
- X = check_array(X)
- if isinstance(self, RegressorMixin):
- return NotImplemented
- preds = np.zeros(X.shape[0])
- for tree in self.trees_:
- preds += self._predict_tree(tree, X)
- if use_clipped_prediction:
- # old behavior, pre v1.3.9
- # constrain to range of probabilities by clipping
- preds = np.clip(preds, a_min=0.0, a_max=1.0)
- else:
- # constrain to range of probabilities with a sigmoid function
- preds = expit(preds)
- return np.vstack((1 - preds, preds)).transpose()
- def _predict_tree(self, root: Node, X):
- """Predict for a single tree"""
- def _predict_tree_single_point(root: Node, x):
- if root.left is None and root.right is None:
- return root.value[0, 0]
- left = x[root.feature] <= root.threshold
- if left:
- if root.left is None: # we don't actually have to worry about this case
- return root.value
- else:
- return _predict_tree_single_point(root.left, x)
- else:
- if (
- root.right is None
- ): # we don't actually have to worry about this case
- return root.value
- else:
- return _predict_tree_single_point(root.right, x)
- preds = np.zeros(X.shape[0])
- for i in range(X.shape[0]):
- preds[i] = _predict_tree_single_point(root, X[i])
- return preds
- @property
- def feature_importances_(self):
- """Gini impurity-based feature importances"""
- check_is_fitted(self)
- avg_feature_importances = np.mean(
- self.importance_data_, axis=0, dtype=np.float64
- )
- return avg_feature_importances / np.sum(avg_feature_importances)
- def plot(
- self,
- cols=2,
- feature_names=None,
- filename=None,
- label="all",
- impurity=False,
- tree_number=None,
- dpi=150,
- fig_size=None,
- ):
- is_single_tree = len(self.trees_) < 2 or tree_number is not None
- n_cols = int(cols)
- n_rows = int(np.ceil(len(self.trees_) / n_cols))
- if feature_names is None:
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- feature_names = self.feature_names_
- n_plots = int(len(self.trees_)) if tree_number is None else 1
- fig, axs = plt.subplots(n_plots, dpi=dpi)
- if fig_size is not None:
- fig.set_size_inches(fig_size, fig_size)
- n_classes = 1 if isinstance(self, RegressorMixin) else 2
- ax_size = int(len(self.trees_))
- for i in range(n_plots):
- r = i // n_cols
- c = i % n_cols
- if not is_single_tree:
- ax = axs[i]
- else:
- ax = axs
- try:
- dt = extract_sklearn_tree_from_figs(
- self, i if tree_number is None else tree_number, n_classes
- )
- plot_tree(
- dt,
- ax=ax,
- feature_names=feature_names,
- label=label,
- impurity=impurity,
- )
- except IndexError:
- ax.axis("off")
- continue
- ttl = f"Tree {i}" if n_plots > 1 else f"Tree {tree_number}"
- ax.set_title(ttl)
- if filename is not None:
- plt.savefig(filename)
- return
- plt.show()</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li>sklearn.base.BaseEstimator</li>
- <li>sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin</li>
- <li>sklearn.utils._metadata_requests._MetadataRequester</li>
- </ul>
- <h3>Subclasses</h3>
- <ul class="hlist">
- <li><a title="imodels.tree.figs.FIGSClassifier" href="#imodels.tree.figs.FIGSClassifier">FIGSClassifier</a></li>
- <li><a title="imodels.tree.figs.FIGSRegressor" href="#imodels.tree.figs.FIGSRegressor">FIGSRegressor</a></li>
- </ul>
- <h3>Instance variables</h3>
- <dl>
- <dt id="imodels.tree.figs.FIGS.feature_importances_"><code class="name">var <span class="ident">feature_importances_</span></code></dt>
- <dd>
- <div class="desc"><p>Gini impurity-based feature importances</p></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">@property
- def feature_importances_(self):
- """Gini impurity-based feature importances"""
- check_is_fitted(self)
- avg_feature_importances = np.mean(
- self.importance_data_, axis=0, dtype=np.float64
- )
- return avg_feature_importances / np.sum(avg_feature_importances)</code></pre>
- </details>
- </dd>
- </dl>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.tree.figs.FIGS.fit"><code class="name flex">
- <span>def <span class="ident">fit</span></span>(<span>self, X, y=None, feature_names=None, verbose=False, sample_weight=None, categorical_features=None)</span>
- </code></dt>
- <dd>
- <div class="desc"><h2 id="params">Params</h2>
- <p>_sample_weight: array-like of shape (n_samples,), default=None
- Sample weights. If None, then samples are equally weighted.
- Splits that would create child nodes with net zero or negative weight
- are ignored while searching for a split in each node.</p></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def fit(
- self,
- X,
- y=None,
- feature_names=None,
- verbose=False,
- sample_weight=None,
- categorical_features=None,
- ):
- """
- Params
- ------
- _sample_weight: array-like of shape (n_samples,), default=None
- Sample weights. If None, then samples are equally weighted.
- Splits that would create child nodes with net zero or negative weight
- are ignored while searching for a split in each node.
- """
- if categorical_features is not None:
- X, self._encoder = self._encode_categories(X, categorical_features)
- X, y, feature_names = check_fit_arguments(self, X, y, feature_names)
- if sample_weight is not None:
- sample_weight = _check_sample_weight(sample_weight, X)
- self.trees_ = [] # list of the root nodes of added trees
- self.complexity_ = 0 # tracks the number of rules in the model
- y_predictions_per_tree = {} # predictions for each tree
- y_residuals_per_tree = {} # based on predictions above
- # set up initial potential_splits
- # everything in potential_splits either is_root (so it can be added directly to self.trees_)
- # or it is a child of a root node that has already been added
- idxs = np.ones(X.shape[0], dtype=bool)
- node_init = self._construct_node_with_stump(
- X=X,
- y=y,
- idxs=idxs,
- tree_num=-1,
- sample_weight=sample_weight,
- max_features=self.max_features,
- )
- potential_splits = [node_init]
- for node in potential_splits:
- node.setattrs(is_root=True)
- potential_splits = sorted(potential_splits, key=lambda x: x.impurity_reduction)
- # start the greedy fitting algorithm
- finished = False
- while len(potential_splits) > 0 and not finished:
- # print('potential_splits', [str(s) for s in potential_splits])
- # get node with max impurity_reduction (since it's sorted)
- split_node = potential_splits.pop()
- # don't split on node
- if split_node.impurity_reduction < self.min_impurity_decrease:
- finished = True
- break
- elif (
- split_node.is_root
- and self.max_trees is not None
- and len(self.trees_) >= self.max_trees
- ):
- # If the node is the root of a new tree and we have reached self.max_trees,
- # don't split on it, but allow later splits to continue growing existing trees
- continue
- # split on node
- if verbose:
- print("\nadding " + str(split_node))
- self.complexity_ += 1
- # if added a tree root
- if split_node.is_root:
- # start a new tree
- self.trees_.append(split_node)
- # update tree_num
- for node_ in [split_node, split_node.left_temp, split_node.right_temp]:
- if node_ is not None:
- node_.tree_num = len(self.trees_) - 1
- # add new root potential node
- node_new_root = Node(
- is_root=True, idxs=np.ones(X.shape[0], dtype=bool), tree_num=-1
- )
- potential_splits.append(node_new_root)
- # add children to potential splits
- # assign left_temp, right_temp to be proper children
- # (basically adds them to tree in predict method)
- split_node.setattrs(left=split_node.left_temp, right=split_node.right_temp)
- # add children to potential_splits
- potential_splits.append(split_node.left)
- potential_splits.append(split_node.right)
- # update predictions for altered tree
- for tree_num_ in range(len(self.trees_)):
- y_predictions_per_tree[tree_num_] = self._predict_tree(
- self.trees_[tree_num_], X
- )
- # dummy 0 preds for possible new trees
- y_predictions_per_tree[-1] = np.zeros(X.shape[0])
- # update residuals for each tree
- # -1 is key for potential new tree
- for tree_num_ in list(range(len(self.trees_))) + [-1]:
- y_residuals_per_tree[tree_num_] = deepcopy(y)
- # subtract predictions of all other trees
- # Since the current tree makes a constant prediction over the node being split,
- # one may ignore its contributions to the residuals without affecting the impurity decrease.
- for tree_num_other_ in range(len(self.trees_)):
- if not tree_num_other_ == tree_num_:
- y_residuals_per_tree[tree_num_] -= y_predictions_per_tree[
- tree_num_other_
- ]
- # recompute all impurities + update potential_split children
- potential_splits_new = []
- for potential_split in potential_splits:
- y_target = y_residuals_per_tree[potential_split.tree_num]
- # re-calculate the best split
- potential_split_updated = self._construct_node_with_stump(
- X=X,
- y=y_target,
- idxs=potential_split.idxs,
- tree_num=potential_split.tree_num,
- sample_weight=sample_weight,
- max_features=self.max_features,
- )
- # need to preserve certain attributes from before (value at this split + is_root)
- # value may change because residuals may have changed, but we want it to store the value from before
- potential_split.setattrs(
- feature=potential_split_updated.feature,
- threshold=potential_split_updated.threshold,
- impurity_reduction=potential_split_updated.impurity_reduction,
- impurity=potential_split_updated.impurity,
- left_temp=potential_split_updated.left_temp,
- right_temp=potential_split_updated.right_temp,
- )
- # this is a valid split
- if potential_split.impurity_reduction is not None:
- potential_splits_new.append(potential_split)
- # sort so largest impurity reduction comes last (should probs make this a heap later)
- potential_splits = sorted(
- potential_splits_new, key=lambda x: x.impurity_reduction
- )
- if verbose:
- print(self)
- if self.max_rules is not None and self.complexity_ >= self.max_rules:
- finished = True
- break
- # annotate final tree with node_id and value_sklearn, and prepare importance_data_
- importance_data = []
- for tree_ in self.trees_:
- node_counter = iter(range(0, int(1e06)))
- def _annotate_node(node: Node, X, y):
- if node is None:
- return
- # TODO does not incorporate sample weights
- value_counts = pd.Series(y).value_counts()
- try:
- neg_count = value_counts[0.0]
- except KeyError:
- neg_count = 0
- try:
- pos_count = value_counts[1.0]
- except KeyError:
- pos_count = 0
- value_sklearn = np.array([neg_count, pos_count], dtype=float)
- node.setattrs(node_id=next(node_counter), value_sklearn=value_sklearn)
- idxs_left = X[:, node.feature] <= node.threshold
- _annotate_node(node.left, X[idxs_left], y[idxs_left])
- _annotate_node(node.right, X[~idxs_left], y[~idxs_left])
- _annotate_node(tree_, X, y)
- # now that the samples per node are known, we can start to compute the importances
- importance_data_tree = np.zeros(len(self.feature_names_))
- def _importances(node: Node):
- if node is None or node.left is None:
- return 0.0
- # TODO does not incorporate sample weights, but will if added to value_sklearn
- importance_data_tree[node.feature] += (
- np.sum(node.value_sklearn) * node.impurity
- - np.sum(node.left.value_sklearn) * node.left.impurity
- - np.sum(node.right.value_sklearn) * node.right.impurity
- )
- return (
- np.sum(node.value_sklearn)
- + _importances(node.left)
- + _importances(node.right)
- )
- # require the tree to have more than 1 node, otherwise just leave importance_data_tree as zeros
- if 1 < next(node_counter):
- tree_samples = _importances(tree_)
- if tree_samples != 0:
- importance_data_tree /= tree_samples
- else:
- importance_data_tree = 0
- importance_data.append(importance_data_tree)
- self.importance_data_ = importance_data
- return self</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGS.plot"><code class="name flex">
- <span>def <span class="ident">plot</span></span>(<span>self, cols=2, feature_names=None, filename=None, label='all', impurity=False, tree_number=None, dpi=150, fig_size=None)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def plot(
- self,
- cols=2,
- feature_names=None,
- filename=None,
- label="all",
- impurity=False,
- tree_number=None,
- dpi=150,
- fig_size=None,
- ):
- is_single_tree = len(self.trees_) < 2 or tree_number is not None
- n_cols = int(cols)
- n_rows = int(np.ceil(len(self.trees_) / n_cols))
- if feature_names is None:
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- feature_names = self.feature_names_
- n_plots = int(len(self.trees_)) if tree_number is None else 1
- fig, axs = plt.subplots(n_plots, dpi=dpi)
- if fig_size is not None:
- fig.set_size_inches(fig_size, fig_size)
- n_classes = 1 if isinstance(self, RegressorMixin) else 2
- ax_size = int(len(self.trees_))
- for i in range(n_plots):
- r = i // n_cols
- c = i % n_cols
- if not is_single_tree:
- ax = axs[i]
- else:
- ax = axs
- try:
- dt = extract_sklearn_tree_from_figs(
- self, i if tree_number is None else tree_number, n_classes
- )
- plot_tree(
- dt,
- ax=ax,
- feature_names=feature_names,
- label=label,
- impurity=impurity,
- )
- except IndexError:
- ax.axis("off")
- continue
- ttl = f"Tree {i}" if n_plots > 1 else f"Tree {tree_number}"
- ax.set_title(ttl)
- if filename is not None:
- plt.savefig(filename)
- return
- plt.show()</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGS.predict"><code class="name flex">
- <span>def <span class="ident">predict</span></span>(<span>self, X, categorical_features=None)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def predict(self, X, categorical_features=None):
- if hasattr(self, "_encoder"):
- X = self._encode_categories(X, categorical_features=categorical_features)
- X = check_array(X)
- preds = np.zeros(X.shape[0])
- for tree in self.trees_:
- preds += self._predict_tree(tree, X)
- if isinstance(self, RegressorMixin):
- return preds
- elif isinstance(self, ClassifierMixin):
- return (preds > 0.5).astype(int)</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGS.predict_proba"><code class="name flex">
- <span>def <span class="ident">predict_proba</span></span>(<span>self, X, categorical_features=None, use_clipped_prediction=False)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Predict probability for classifiers:
- Default behavior is to constrain the outputs to the range of probabilities, i.e. 0 to 1, with a sigmoid function.
- Set use_clipped_prediction=True to use prior behavior of clipping between 0 and 1 instead.</p></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def predict_proba(self, X, categorical_features=None, use_clipped_prediction=False):
- """Predict probability for classifiers:
- Default behavior is to constrain the outputs to the range of probabilities, i.e. 0 to 1, with a sigmoid function.
- Set use_clipped_prediction=True to use prior behavior of clipping between 0 and 1 instead.
- """
- if hasattr(self, "_encoder"):
- X = self._encode_categories(X, categorical_features=categorical_features)
- X = check_array(X)
- if isinstance(self, RegressorMixin):
- return NotImplemented
- preds = np.zeros(X.shape[0])
- for tree in self.trees_:
- preds += self._predict_tree(tree, X)
- if use_clipped_prediction:
- # old behavior, pre v1.3.9
- # constrain to range of probabilities by clipping
- preds = np.clip(preds, a_min=0.0, a_max=1.0)
- else:
- # constrain to range of probabilities with a sigmoid function
- preds = expit(preds)
- return np.vstack((1 - preds, preds)).transpose()</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGS.print_tree"><code class="name flex">
- <span>def <span class="ident">print_tree</span></span>(<span>self, X, y, feature_names=None)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def print_tree(self, X, y, feature_names=None):
- s = "------------\n" + "\n\t+\n".join(
- [self._tree_to_str_with_data(X, y, t) for t in self.trees_]
- )
- if feature_names is None:
- if hasattr(self, "feature_names_") and self.feature_names_ is not None:
- feature_names = self.feature_names_
- if feature_names is not None:
- for i in range(len(feature_names))[::-1]:
- s = s.replace(f"X_{i}", feature_names[i])
- return s</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGS.set_fit_request"><code class="name flex">
- <span>def <span class="ident">set_fit_request</span></span>(<span>self: <a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a>, *, categorical_features: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', feature_names: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', verbose: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> <a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></span>
- </code></dt>
- <dd>
- <div class="desc"><p>Request metadata passed to the <code>fit</code> method.</p>
- <p>Note that this method is only relevant if
- <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>).
- Please see :ref:<code>User Guide <metadata_routing></code> on how the routing
- mechanism works.</p>
- <p>The options for each parameter are:</p>
- <ul>
- <li>
- <p><code>True</code>: metadata is requested, and passed to <code>fit</code> if provided. The request is ignored if metadata is not provided.</p>
- </li>
- <li>
- <p><code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>fit</code>.</p>
- </li>
- <li>
- <p><code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p>
- </li>
- <li>
- <p><code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p>
- </li>
- </ul>
- <p>The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the
- existing request. This allows you to change the request for some
- parameters and not others.</p>
- <div class="admonition versionadded">
- <p class="admonition-title">Added in version: 1.3</p>
- </div>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>This method is only relevant if this estimator is used as a
- sub-estimator of a meta-estimator, e.g. used inside a
- :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p>
- </div>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>categorical_features</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>categorical_features</code> parameter in <code>fit</code>.</dd>
- <dt><strong><code>feature_names</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>feature_names</code> parameter in <code>fit</code>.</dd>
- <dt><strong><code>sample_weight</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>sample_weight</code> parameter in <code>fit</code>.</dd>
- <dt><strong><code>verbose</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>verbose</code> parameter in <code>fit</code>.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>self</code></strong> : <code>object</code></dt>
- <dd>The updated object.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def func(**kw):
- """Updates the request for provided parameters
- This docstring is overwritten below.
- See REQUESTER_DOC for expected functionality
- """
- if not _routing_enabled():
- raise RuntimeError(
- "This method is only available when metadata routing is enabled."
- " You can enable it using"
- " sklearn.set_config(enable_metadata_routing=True)."
- )
- if self.validate_keys and (set(kw) - set(self.keys)):
- raise TypeError(
- f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
- f" are: {set(self.keys)}"
- )
- requests = instance._get_metadata_request()
- method_metadata_request = getattr(requests, self.name)
- for prop, alias in kw.items():
- if alias is not UNCHANGED:
- method_metadata_request.add_request(param=prop, alias=alias)
- instance._metadata_request = requests
- return instance</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGS.set_predict_proba_request"><code class="name flex">
- <span>def <span class="ident">set_predict_proba_request</span></span>(<span>self: <a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a>, *, categorical_features: Union[bool, ForwardRef(None), str] = '$UNCHANGED$', use_clipped_prediction: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> <a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></span>
- </code></dt>
- <dd>
- <div class="desc"><p>Request metadata passed to the <code>predict_proba</code> method.</p>
- <p>Note that this method is only relevant if
- <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>).
- Please see :ref:<code>User Guide <metadata_routing></code> on how the routing
- mechanism works.</p>
- <p>The options for each parameter are:</p>
- <ul>
- <li>
- <p><code>True</code>: metadata is requested, and passed to <code>predict_proba</code> if provided. The request is ignored if metadata is not provided.</p>
- </li>
- <li>
- <p><code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>predict_proba</code>.</p>
- </li>
- <li>
- <p><code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p>
- </li>
- <li>
- <p><code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p>
- </li>
- </ul>
- <p>The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the
- existing request. This allows you to change the request for some
- parameters and not others.</p>
- <div class="admonition versionadded">
- <p class="admonition-title">Added in version: 1.3</p>
- </div>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>This method is only relevant if this estimator is used as a
- sub-estimator of a meta-estimator, e.g. used inside a
- :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p>
- </div>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>categorical_features</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>categorical_features</code> parameter in <code>predict_proba</code>.</dd>
- <dt><strong><code>use_clipped_prediction</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>use_clipped_prediction</code> parameter in <code>predict_proba</code>.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>self</code></strong> : <code>object</code></dt>
- <dd>The updated object.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def func(**kw):
- """Updates the request for provided parameters
- This docstring is overwritten below.
- See REQUESTER_DOC for expected functionality
- """
- if not _routing_enabled():
- raise RuntimeError(
- "This method is only available when metadata routing is enabled."
- " You can enable it using"
- " sklearn.set_config(enable_metadata_routing=True)."
- )
- if self.validate_keys and (set(kw) - set(self.keys)):
- raise TypeError(
- f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
- f" are: {set(self.keys)}"
- )
- requests = instance._get_metadata_request()
- method_metadata_request = getattr(requests, self.name)
- for prop, alias in kw.items():
- if alias is not UNCHANGED:
- method_metadata_request.add_request(param=prop, alias=alias)
- instance._metadata_request = requests
- return instance</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGS.set_predict_request"><code class="name flex">
- <span>def <span class="ident">set_predict_request</span></span>(<span>self: <a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a>, *, categorical_features: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> <a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></span>
- </code></dt>
- <dd>
- <div class="desc"><p>Request metadata passed to the <code>predict</code> method.</p>
- <p>Note that this method is only relevant if
- <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>).
- Please see :ref:<code>User Guide <metadata_routing></code> on how the routing
- mechanism works.</p>
- <p>The options for each parameter are:</p>
- <ul>
- <li>
- <p><code>True</code>: metadata is requested, and passed to <code>predict</code> if provided. The request is ignored if metadata is not provided.</p>
- </li>
- <li>
- <p><code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>predict</code>.</p>
- </li>
- <li>
- <p><code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p>
- </li>
- <li>
- <p><code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p>
- </li>
- </ul>
- <p>The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the
- existing request. This allows you to change the request for some
- parameters and not others.</p>
- <div class="admonition versionadded">
- <p class="admonition-title">Added in version: 1.3</p>
- </div>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>This method is only relevant if this estimator is used as a
- sub-estimator of a meta-estimator, e.g. used inside a
- :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p>
- </div>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>categorical_features</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>categorical_features</code> parameter in <code>predict</code>.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>self</code></strong> : <code>object</code></dt>
- <dd>The updated object.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def func(**kw):
- """Updates the request for provided parameters
- This docstring is overwritten below.
- See REQUESTER_DOC for expected functionality
- """
- if not _routing_enabled():
- raise RuntimeError(
- "This method is only available when metadata routing is enabled."
- " You can enable it using"
- " sklearn.set_config(enable_metadata_routing=True)."
- )
- if self.validate_keys and (set(kw) - set(self.keys)):
- raise TypeError(
- f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
- f" are: {set(self.keys)}"
- )
- requests = instance._get_metadata_request()
- method_metadata_request = getattr(requests, self.name)
- for prop, alias in kw.items():
- if alias is not UNCHANGED:
- method_metadata_request.add_request(param=prop, alias=alias)
- instance._metadata_request = requests
- return instance</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="imodels.tree.figs.FIGSCV"><code class="flex name class">
- <span>class <span class="ident">FIGSCV</span></span>
- <span>(</span><span>figs, n_rules_list: List[int] = [6, 12, 24, 30, 50], n_trees_list: List[int] = [5, 5, 5, 5, 5], cv: int = 3, scoring=None, *args, **kwargs)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class FIGSCV:
- def __init__(
- self,
- figs,
- n_rules_list: List[int] = [6, 12, 24, 30, 50],
- n_trees_list: List[int] = [5, 5, 5, 5, 5],
- cv: int = 3,
- scoring=None,
- *args,
- **kwargs,
- ):
- if len(n_rules_list) != len(n_trees_list):
- raise ValueError(
- f"len(n_rules_list) = {len(n_rules_list)} != len(n_trees_list) = {len(n_trees_list)}"
- )
- self._figs_class = figs
- self.n_rules_list = np.array(n_rules_list)
- self.n_trees_list = np.array(n_trees_list)
- self.cv = cv
- self.scoring = scoring
- def fit(self, X, y):
- self.scores_ = []
- for _i, n_rules in enumerate(self.n_rules_list):
- est = self._figs_class(max_rules=n_rules, max_trees=self.n_trees_list[_i])
- cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
- mean_score = np.mean(cv_scores)
- if len(self.scores_) == 0:
- self.figs = est
- elif mean_score > np.max(self.scores_):
- self.figs = est
- self.scores_.append(mean_score)
- self.figs.fit(X=X, y=y)
- def predict_proba(self, X):
- return self.figs.predict_proba(X)
- def predict(self, X):
- return self.figs.predict(X)
- @property
- def max_rules(self):
- return self.figs.max_rules
- @property
- def max_trees(self):
- return self.figs.max_trees</code></pre>
- </details>
- <h3>Subclasses</h3>
- <ul class="hlist">
- <li><a title="imodels.tree.figs.FIGSClassifierCV" href="#imodels.tree.figs.FIGSClassifierCV">FIGSClassifierCV</a></li>
- <li><a title="imodels.tree.figs.FIGSRegressorCV" href="#imodels.tree.figs.FIGSRegressorCV">FIGSRegressorCV</a></li>
- </ul>
- <h3>Instance variables</h3>
- <dl>
- <dt id="imodels.tree.figs.FIGSCV.max_rules"><code class="name">var <span class="ident">max_rules</span></code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">@property
- def max_rules(self):
- return self.figs.max_rules</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGSCV.max_trees"><code class="name">var <span class="ident">max_trees</span></code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">@property
- def max_trees(self):
- return self.figs.max_trees</code></pre>
- </details>
- </dd>
- </dl>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.tree.figs.FIGSCV.fit"><code class="name flex">
- <span>def <span class="ident">fit</span></span>(<span>self, X, y)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def fit(self, X, y):
- self.scores_ = []
- for _i, n_rules in enumerate(self.n_rules_list):
- est = self._figs_class(max_rules=n_rules, max_trees=self.n_trees_list[_i])
- cv_scores = cross_val_score(est, X, y, cv=self.cv, scoring=self.scoring)
- mean_score = np.mean(cv_scores)
- if len(self.scores_) == 0:
- self.figs = est
- elif mean_score > np.max(self.scores_):
- self.figs = est
- self.scores_.append(mean_score)
- self.figs.fit(X=X, y=y)</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGSCV.predict"><code class="name flex">
- <span>def <span class="ident">predict</span></span>(<span>self, X)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def predict(self, X):
- return self.figs.predict(X)</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.FIGSCV.predict_proba"><code class="name flex">
- <span>def <span class="ident">predict_proba</span></span>(<span>self, X)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def predict_proba(self, X):
- return self.figs.predict_proba(X)</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- <dt id="imodels.tree.figs.FIGSClassifier"><code class="flex name class">
- <span>class <span class="ident">FIGSClassifier</span></span>
- <span>(</span><span>max_rules: int = 12, max_trees: int = None, min_impurity_decrease: float = 0.0, random_state=None, max_features: str = None)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>FIGS (sum of trees) classifier.
- Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
- Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
- The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
- Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
- <a href="https://arxiv.org/abs/2201.11931">https://arxiv.org/abs/2201.11931</a></p>
- <h2 id="params">Params</h2>
- <p>max_rules: int
- Max total number of rules across all trees
- max_trees: int
- Max total number of trees
- min_impurity_decrease: float
- A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
- max_features
- The number of features to consider when looking for the best split (see <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html">https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html</a>)</p></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class FIGSClassifier(FIGS, ClassifierMixin):
- ...</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li><a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></li>
- <li>sklearn.base.BaseEstimator</li>
- <li>sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin</li>
- <li>sklearn.utils._metadata_requests._MetadataRequester</li>
- <li>sklearn.base.ClassifierMixin</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.tree.figs.FIGSClassifier.set_score_request"><code class="name flex">
- <span>def <span class="ident">set_score_request</span></span>(<span>self: <a title="imodels.tree.figs.FIGSClassifier" href="#imodels.tree.figs.FIGSClassifier">FIGSClassifier</a>, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> <a title="imodels.tree.figs.FIGSClassifier" href="#imodels.tree.figs.FIGSClassifier">FIGSClassifier</a></span>
- </code></dt>
- <dd>
- <div class="desc"><p>Request metadata passed to the <code>score</code> method.</p>
- <p>Note that this method is only relevant if
- <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>).
- Please see :ref:<code>User Guide <metadata_routing></code> on how the routing
- mechanism works.</p>
- <p>The options for each parameter are:</p>
- <ul>
- <li>
- <p><code>True</code>: metadata is requested, and passed to <code>score</code> if provided. The request is ignored if metadata is not provided.</p>
- </li>
- <li>
- <p><code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>score</code>.</p>
- </li>
- <li>
- <p><code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p>
- </li>
- <li>
- <p><code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p>
- </li>
- </ul>
- <p>The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the
- existing request. This allows you to change the request for some
- parameters and not others.</p>
- <div class="admonition versionadded">
- <p class="admonition-title">Added in version: 1.3</p>
- </div>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>This method is only relevant if this estimator is used as a
- sub-estimator of a meta-estimator, e.g. used inside a
- :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p>
- </div>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>sample_weight</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>sample_weight</code> parameter in <code>score</code>.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>self</code></strong> : <code>object</code></dt>
- <dd>The updated object.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def func(**kw):
- """Updates the request for provided parameters
- This docstring is overwritten below.
- See REQUESTER_DOC for expected functionality
- """
- if not _routing_enabled():
- raise RuntimeError(
- "This method is only available when metadata routing is enabled."
- " You can enable it using"
- " sklearn.set_config(enable_metadata_routing=True)."
- )
- if self.validate_keys and (set(kw) - set(self.keys)):
- raise TypeError(
- f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
- f" are: {set(self.keys)}"
- )
- requests = instance._get_metadata_request()
- method_metadata_request = getattr(requests, self.name)
- for prop, alias in kw.items():
- if alias is not UNCHANGED:
- method_metadata_request.add_request(param=prop, alias=alias)
- instance._metadata_request = requests
- return instance</code></pre>
- </details>
- </dd>
- </dl>
- <h3>Inherited members</h3>
- <ul class="hlist">
- <li><code><b><a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></b></code>:
- <ul class="hlist">
- <li><code><a title="imodels.tree.figs.FIGS.feature_importances_" href="#imodels.tree.figs.FIGS.feature_importances_">feature_importances_</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.fit" href="#imodels.tree.figs.FIGS.fit">fit</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.predict_proba" href="#imodels.tree.figs.FIGS.predict_proba">predict_proba</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_fit_request" href="#imodels.tree.figs.FIGS.set_fit_request">set_fit_request</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_predict_proba_request" href="#imodels.tree.figs.FIGS.set_predict_proba_request">set_predict_proba_request</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_predict_request" href="#imodels.tree.figs.FIGS.set_predict_request">set_predict_request</a></code></li>
- </ul>
- </li>
- </ul>
- </dd>
- <dt id="imodels.tree.figs.FIGSClassifierCV"><code class="flex name class">
- <span>class <span class="ident">FIGSClassifierCV</span></span>
- <span>(</span><span>n_rules_list: List[int] = [6, 12, 24, 30, 50], n_trees_list: List[int] = [5, 5, 5, 5, 5], cv: int = 3, scoring='accuracy', *args, **kwargs)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class FIGSClassifierCV(FIGSCV):
- def __init__(
- self,
- n_rules_list: List[int] = [6, 12, 24, 30, 50],
- n_trees_list: List[int] = [5, 5, 5, 5, 5],
- cv: int = 3,
- scoring="accuracy",
- *args,
- **kwargs,
- ):
- super(FIGSClassifierCV, self).__init__(
- figs=FIGSClassifier,
- n_rules_list=n_rules_list,
- n_trees_list=n_trees_list,
- cv=cv,
- scoring=scoring,
- *args,
- **kwargs,
- )</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li><a title="imodels.tree.figs.FIGSCV" href="#imodels.tree.figs.FIGSCV">FIGSCV</a></li>
- </ul>
- </dd>
- <dt id="imodels.tree.figs.FIGSRegressor"><code class="flex name class">
- <span>class <span class="ident">FIGSRegressor</span></span>
- <span>(</span><span>max_rules: int = 12, max_trees: int = None, min_impurity_decrease: float = 0.0, random_state=None, max_features: str = None)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>FIGS (sum of trees) classifier.
- Fast Interpretable Greedy-Tree Sums (FIGS) is an algorithm for fitting concise rule-based models.
- Specifically, FIGS generalizes CART to simultaneously grow a flexible number of trees in a summation.
- The total number of splits across all the trees can be restricted by a pre-specified threshold, keeping the model interpretable.
- Experiments across real-world datasets show that FIGS achieves state-of-the-art prediction performance when restricted to just a few splits (e.g. less than 20).
- <a href="https://arxiv.org/abs/2201.11931">https://arxiv.org/abs/2201.11931</a></p>
- <h2 id="params">Params</h2>
- <p>max_rules: int
- Max total number of rules across all trees
- max_trees: int
- Max total number of trees
- min_impurity_decrease: float
- A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
- max_features
- The number of features to consider when looking for the best split (see <a href="https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html">https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html</a>)</p></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class FIGSRegressor(FIGS, RegressorMixin):
- ...</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li><a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></li>
- <li>sklearn.base.BaseEstimator</li>
- <li>sklearn.utils._estimator_html_repr._HTMLDocumentationLinkMixin</li>
- <li>sklearn.utils._metadata_requests._MetadataRequester</li>
- <li>sklearn.base.RegressorMixin</li>
- </ul>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.tree.figs.FIGSRegressor.set_score_request"><code class="name flex">
- <span>def <span class="ident">set_score_request</span></span>(<span>self: <a title="imodels.tree.figs.FIGSRegressor" href="#imodels.tree.figs.FIGSRegressor">FIGSRegressor</a>, *, sample_weight: Union[bool, ForwardRef(None), str] = '$UNCHANGED$') ‑> <a title="imodels.tree.figs.FIGSRegressor" href="#imodels.tree.figs.FIGSRegressor">FIGSRegressor</a></span>
- </code></dt>
- <dd>
- <div class="desc"><p>Request metadata passed to the <code>score</code> method.</p>
- <p>Note that this method is only relevant if
- <code>enable_metadata_routing=True</code> (see :func:<code>sklearn.set_config</code>).
- Please see :ref:<code>User Guide <metadata_routing></code> on how the routing
- mechanism works.</p>
- <p>The options for each parameter are:</p>
- <ul>
- <li>
- <p><code>True</code>: metadata is requested, and passed to <code>score</code> if provided. The request is ignored if metadata is not provided.</p>
- </li>
- <li>
- <p><code>False</code>: metadata is not requested and the meta-estimator will not pass it to <code>score</code>.</p>
- </li>
- <li>
- <p><code>None</code>: metadata is not requested, and the meta-estimator will raise an error if the user provides it.</p>
- </li>
- <li>
- <p><code>str</code>: metadata should be passed to the meta-estimator with this given alias instead of the original name.</p>
- </li>
- </ul>
- <p>The default (<code>sklearn.utils.metadata_routing.UNCHANGED</code>) retains the
- existing request. This allows you to change the request for some
- parameters and not others.</p>
- <div class="admonition versionadded">
- <p class="admonition-title">Added in version: 1.3</p>
- </div>
- <div class="admonition note">
- <p class="admonition-title">Note</p>
- <p>This method is only relevant if this estimator is used as a
- sub-estimator of a meta-estimator, e.g. used inside a
- :class:<code>~sklearn.pipeline.Pipeline</code>. Otherwise it has no effect.</p>
- </div>
- <h2 id="parameters">Parameters</h2>
- <dl>
- <dt><strong><code>sample_weight</code></strong> : <code>str, True, False,</code> or <code>None</code>,
- default=<code>sklearn.utils.metadata_routing.UNCHANGED</code></dt>
- <dd>Metadata routing for <code>sample_weight</code> parameter in <code>score</code>.</dd>
- </dl>
- <h2 id="returns">Returns</h2>
- <dl>
- <dt><strong><code>self</code></strong> : <code>object</code></dt>
- <dd>The updated object.</dd>
- </dl></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def func(**kw):
- """Updates the request for provided parameters
- This docstring is overwritten below.
- See REQUESTER_DOC for expected functionality
- """
- if not _routing_enabled():
- raise RuntimeError(
- "This method is only available when metadata routing is enabled."
- " You can enable it using"
- " sklearn.set_config(enable_metadata_routing=True)."
- )
- if self.validate_keys and (set(kw) - set(self.keys)):
- raise TypeError(
- f"Unexpected args: {set(kw) - set(self.keys)}. Accepted arguments"
- f" are: {set(self.keys)}"
- )
- requests = instance._get_metadata_request()
- method_metadata_request = getattr(requests, self.name)
- for prop, alias in kw.items():
- if alias is not UNCHANGED:
- method_metadata_request.add_request(param=prop, alias=alias)
- instance._metadata_request = requests
- return instance</code></pre>
- </details>
- </dd>
- </dl>
- <h3>Inherited members</h3>
- <ul class="hlist">
- <li><code><b><a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></b></code>:
- <ul class="hlist">
- <li><code><a title="imodels.tree.figs.FIGS.feature_importances_" href="#imodels.tree.figs.FIGS.feature_importances_">feature_importances_</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.fit" href="#imodels.tree.figs.FIGS.fit">fit</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.predict_proba" href="#imodels.tree.figs.FIGS.predict_proba">predict_proba</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_fit_request" href="#imodels.tree.figs.FIGS.set_fit_request">set_fit_request</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_predict_proba_request" href="#imodels.tree.figs.FIGS.set_predict_proba_request">set_predict_proba_request</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_predict_request" href="#imodels.tree.figs.FIGS.set_predict_request">set_predict_request</a></code></li>
- </ul>
- </li>
- </ul>
- </dd>
- <dt id="imodels.tree.figs.FIGSRegressorCV"><code class="flex name class">
- <span>class <span class="ident">FIGSRegressorCV</span></span>
- <span>(</span><span>n_rules_list: List[int] = [6, 12, 24, 30, 50], n_trees_list: List[int] = [5, 5, 5, 5, 5], cv: int = 3, scoring='r2', *args, **kwargs)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class FIGSRegressorCV(FIGSCV):
- def __init__(
- self,
- n_rules_list: List[int] = [6, 12, 24, 30, 50],
- n_trees_list: List[int] = [5, 5, 5, 5, 5],
- cv: int = 3,
- scoring="r2",
- *args,
- **kwargs,
- ):
- super(FIGSRegressorCV, self).__init__(
- figs=FIGSRegressor,
- n_rules_list=n_rules_list,
- n_trees_list=n_trees_list,
- cv=cv,
- scoring=scoring,
- *args,
- **kwargs,
- )</code></pre>
- </details>
- <h3>Ancestors</h3>
- <ul class="hlist">
- <li><a title="imodels.tree.figs.FIGSCV" href="#imodels.tree.figs.FIGSCV">FIGSCV</a></li>
- </ul>
- </dd>
- <dt id="imodels.tree.figs.Node"><code class="flex name class">
- <span>class <span class="ident">Node</span></span>
- <span>(</span><span>feature: int = None, threshold: int = None, value=None, value_sklearn=None, idxs=None, is_root: bool = False, left=None, impurity: float = None, impurity_reduction: float = None, tree_num: int = None, node_id: int = None, right=None)</span>
- </code></dt>
- <dd>
- <div class="desc"><p>Node class for splitting</p></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">class Node:
- def __init__(
- self,
- feature: int = None,
- threshold: int = None,
- value=None,
- value_sklearn=None,
- idxs=None,
- is_root: bool = False,
- left=None,
- impurity: float = None,
- impurity_reduction: float = None,
- tree_num: int = None,
- node_id: int = None,
- right=None,
- ):
- """Node class for splitting"""
- # split or linear
- self.is_root = is_root
- self.idxs = idxs
- self.tree_num = tree_num
- self.node_id = None
- self.feature = feature
- self.impurity = impurity
- self.impurity_reduction = impurity_reduction
- self.value_sklearn = value_sklearn
- # different meanings
- self.value = value # for split this is mean, for linear this is weight
- # split-specific
- self.threshold = threshold
- self.left = left
- self.right = right
- self.left_temp = None
- self.right_temp = None
- def setattrs(self, **kwargs):
- for k, v in kwargs.items():
- setattr(self, k, v)
- def __str__(self):
- if self.is_root:
- return f"X_{self.feature} <= {self.threshold:0.3f} (Tree #{self.tree_num} root)"
- elif self.left is None and self.right is None:
- return f"Val: {self.value[0][0]:0.3f} (leaf)"
- else:
- return f"X_{self.feature} <= {self.threshold:0.3f} (split)"
- def print_root(self, y):
- try:
- one_count = pd.Series(y).value_counts()[1.0]
- except KeyError:
- one_count = 0
- one_proportion = (
- f" {one_count}/{y.shape[0]} ({round(100 * one_count / y.shape[0], 2)}%)"
- )
- if self.is_root:
- return f"X_{self.feature} <= {self.threshold:0.3f}" + one_proportion
- elif self.left is None and self.right is None:
- return f"ΔRisk = {self.value[0][0]:0.2f}" + one_proportion
- else:
- return f"X_{self.feature} <= {self.threshold:0.3f}" + one_proportion
- def __repr__(self):
- return self.__str__()</code></pre>
- </details>
- <h3>Methods</h3>
- <dl>
- <dt id="imodels.tree.figs.Node.print_root"><code class="name flex">
- <span>def <span class="ident">print_root</span></span>(<span>self, y)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def print_root(self, y):
- try:
- one_count = pd.Series(y).value_counts()[1.0]
- except KeyError:
- one_count = 0
- one_proportion = (
- f" {one_count}/{y.shape[0]} ({round(100 * one_count / y.shape[0], 2)}%)"
- )
- if self.is_root:
- return f"X_{self.feature} <= {self.threshold:0.3f}" + one_proportion
- elif self.left is None and self.right is None:
- return f"ΔRisk = {self.value[0][0]:0.2f}" + one_proportion
- else:
- return f"X_{self.feature} <= {self.threshold:0.3f}" + one_proportion</code></pre>
- </details>
- </dd>
- <dt id="imodels.tree.figs.Node.setattrs"><code class="name flex">
- <span>def <span class="ident">setattrs</span></span>(<span>self, **kwargs)</span>
- </code></dt>
- <dd>
- <div class="desc"></div>
- <details class="source">
- <summary>
- <span>Expand source code</span>
- </summary>
- <pre><code class="python">def setattrs(self, **kwargs):
- for k, v in kwargs.items():
- setattr(self, k, v)</code></pre>
- </details>
- </dd>
- </dl>
- </dd>
- </dl>
- </section>
- </article>
- <nav id="sidebar">
- <h1>Index 🔍</h1>
- <div class="toc">
- <ul></ul>
- </div>
- <ul id="index">
- <li><h3>Super-module</h3>
- <ul>
- <li><code><a title="imodels.tree" href="index.html">imodels.tree</a></code></li>
- </ul>
- </li>
- <li><h3><a href="#header-classes">Classes</a></h3>
- <ul>
- <li>
- <h4><code><a title="imodels.tree.figs.FIGS" href="#imodels.tree.figs.FIGS">FIGS</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.tree.figs.FIGS.feature_importances_" href="#imodels.tree.figs.FIGS.feature_importances_">feature_importances_</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.fit" href="#imodels.tree.figs.FIGS.fit">fit</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.plot" href="#imodels.tree.figs.FIGS.plot">plot</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.predict" href="#imodels.tree.figs.FIGS.predict">predict</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.predict_proba" href="#imodels.tree.figs.FIGS.predict_proba">predict_proba</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.print_tree" href="#imodels.tree.figs.FIGS.print_tree">print_tree</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_fit_request" href="#imodels.tree.figs.FIGS.set_fit_request">set_fit_request</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_predict_proba_request" href="#imodels.tree.figs.FIGS.set_predict_proba_request">set_predict_proba_request</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGS.set_predict_request" href="#imodels.tree.figs.FIGS.set_predict_request">set_predict_request</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="imodels.tree.figs.FIGSCV" href="#imodels.tree.figs.FIGSCV">FIGSCV</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.tree.figs.FIGSCV.fit" href="#imodels.tree.figs.FIGSCV.fit">fit</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGSCV.max_rules" href="#imodels.tree.figs.FIGSCV.max_rules">max_rules</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGSCV.max_trees" href="#imodels.tree.figs.FIGSCV.max_trees">max_trees</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGSCV.predict" href="#imodels.tree.figs.FIGSCV.predict">predict</a></code></li>
- <li><code><a title="imodels.tree.figs.FIGSCV.predict_proba" href="#imodels.tree.figs.FIGSCV.predict_proba">predict_proba</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="imodels.tree.figs.FIGSClassifier" href="#imodels.tree.figs.FIGSClassifier">FIGSClassifier</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.tree.figs.FIGSClassifier.set_score_request" href="#imodels.tree.figs.FIGSClassifier.set_score_request">set_score_request</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="imodels.tree.figs.FIGSClassifierCV" href="#imodels.tree.figs.FIGSClassifierCV">FIGSClassifierCV</a></code></h4>
- </li>
- <li>
- <h4><code><a title="imodels.tree.figs.FIGSRegressor" href="#imodels.tree.figs.FIGSRegressor">FIGSRegressor</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.tree.figs.FIGSRegressor.set_score_request" href="#imodels.tree.figs.FIGSRegressor.set_score_request">set_score_request</a></code></li>
- </ul>
- </li>
- <li>
- <h4><code><a title="imodels.tree.figs.FIGSRegressorCV" href="#imodels.tree.figs.FIGSRegressorCV">FIGSRegressorCV</a></code></h4>
- </li>
- <li>
- <h4><code><a title="imodels.tree.figs.Node" href="#imodels.tree.figs.Node">Node</a></code></h4>
- <ul class="">
- <li><code><a title="imodels.tree.figs.Node.print_root" href="#imodels.tree.figs.Node.print_root">print_root</a></code></li>
- <li><code><a title="imodels.tree.figs.Node.setattrs" href="#imodels.tree.figs.Node.setattrs">setattrs</a></code></li>
- </ul>
- </li>
- </ul>
- </li>
- </ul>
- <p><img align="center" width=100% src="https://csinva.io/imodels/img/anim.gif"> </img></p>
- <!-- add wave animation -->
- </nav>
- </main>
- <footer id="footer">
- </footer>
- </body>
- </html>
- <!-- add github corner -->
- <a href="https://github.com/csinva/imodels" class="github-corner" aria-label="View source on GitHub"><svg width="120" height="120" viewBox="0 0 250 250" style="fill:#70B7FD; color:#fff; position: absolute; top: 0; border: 0; right: 0;" aria-hidden="true"><path d="M0,0 L115,115 L130,115 L142,142 L250,250 L250,0 Z"></path><path d="m128.3,109.0 c113.8,99.7 119.0,89.6 119.0,89.6 c122.0,82.7 120.5,78.6 120.5,78.6 c119.2,72.0 123.4,76.3 123.4,76.3 c127.3,80.9 125.5,87.3 125.5,87.3 c122.9,97.6 130.6,101.9 134.4,103.2" fill="currentcolor" style="transform-origin: 130px 106px;" class="octo-arm"></path><path d="M115.0,115.0 C114.9,115.1 118.7,116.5 119.8,115.4 L133.7,101.6 C136.9,99.2 139.9,98.4 142.2,98.6 C133.8,88.0 127.5,74.4 143.8,58.0 C148.5,53.4 154.0,51.2 159.7,51.0 C160.3,49.4 163.2,43.6 171.4,40.1 C171.4,40.1 176.1,42.5 178.8,56.2 C183.1,58.6 187.2,61.8 190.9,65.4 C194.5,69.0 197.7,73.2 200.1,77.6 C213.8,80.2 216.3,84.9 216.3,84.9 C212.7,93.1 206.9,96.0 205.4,96.6 C205.1,102.4 203.0,107.8 198.3,112.5 C181.9,128.9 168.3,122.5 157.7,114.1 C157.9,116.9 156.7,120.9 152.7,124.9 L141.0,136.5 C139.8,137.7 141.6,141.9 141.8,141.8 Z" fill="currentColor" class="octo-body"></path></svg></a><style>.github-corner:hover .octo-arm{animation:octocat-wave 560ms ease-in-out}@keyframes octocat-wave{0%,100%{transform:rotate(0)}20%,60%{transform:rotate(-25deg)}40%,80%{transform:rotate(10deg)}}@media (max-width:500px){.github-corner:hover .octo-arm{animation:none}.github-corner .octo-arm{animation:octocat-wave 560ms ease-in-out}}</style>
- <!-- add wave animation stylesheet -->
- <link rel="stylesheet" href="github.css">
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